Liver Droplet Notebook

tissue_of_interest = "Liver"
library(here)

here() starts at /home/rstudio/tabula-muris

source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))

Loading required package: ggplot2

Loading required package: cowplot

Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

Loading required package: Matrix

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ tibble  1.3.4     ✔ purrr   0.2.4
✔ tidyr   0.7.2     ✔ stringr 1.2.0
✔ readr   1.1.1     ✔ forcats 0.2.0

── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ tidyr::expand()   masks Matrix::expand()
✖ dplyr::filter()   masks stats::filter()
✖ cowplot::ggsave() masks ggplot2::ggsave()
✖ dplyr::lag()      masks stats::lag()

tiss = load_tissue_droplet(tissue_of_interest)

[1] "Scaling data matrix"

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Visualize top genes in principal components

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

PCElbowPlot(object = tiss)

Choose the number of principal components to use.

# Set number of principal components. 
n.pcs = 10

The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale. Higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

# Set resolution 
res.used <- 3.5

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE)

We use TSNE solely to visualize the data.

# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 10, perplexity=30)

TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)

Compare to previous annotations

tiss = compare_previous_annotation(tiss, tissue_of_interest, "droplet")

Warning: Missing column names filled in: 'X1' [1]

Parsed with column specification:
cols(
  X1 = col_character(),
  channel = col_character(),
  cell_ontology_class = col_character(),
  cell_ontology_id = col_character(),
  free_annotation = col_character(),
  cluster.ids = col_integer()
)

                                   8                                    9 
                                  93                                   28 
                duct epithelial cell endothelial cell of hepatic sinusoid 
                                  26                                   27 
                          hepatocyte 
                                1671 
                                      
                                         0   1   2   3   4   5   6   7   8
  8                                      0   0   0   0   0   0   0   0  91
  9                                      0   0   0   0   0   0   0   0   0
  duct epithelial cell                   0   0   0   0   0   0   0   0   0
  endothelial cell of hepatic sinusoid   0   0   0   0   0   0   0   0   0
  hepatocyte                           164 140 131 124 122 113 113 112   8
                                      
                                         9  10  11  12  13  14  15  16  17
  8                                      0   0   0   0   1   0   1   0   0
  9                                      0   0   0   0   0   0   0   0   0
  duct epithelial cell                   0   0   0   0   0   0   0   0   0
  endothelial cell of hepatic sinusoid   0   0   0   0   0   0   0   0   0
  hepatocyte                            96  96  86  78  76  72  56  43  41
                                      
                                        18  19  20
  8                                      0   0   0
  9                                     28   0   0
  duct epithelial cell                   0   0  26
  endothelial cell of hepatic sinusoid   0  27   0
  hepatocyte                             0   0   0

TSNEPlot(object = tiss, do.return = TRUE, group.by = "previous_annotation")

table(tiss@meta.data[, "previous_annotation"], tiss@ident)

                                      
                                         0   1   2   3   4   5   6   7   8
  8                                      0   0   0   0   0   0   0   0  91
  9                                      0   0   0   0   0   0   0   0   0
  duct epithelial cell                   0   0   0   0   0   0   0   0   0
  endothelial cell of hepatic sinusoid   0   0   0   0   0   0   0   0   0
  hepatocyte                           164 140 131 124 122 113 113 112   8
                                      
                                         9  10  11  12  13  14  15  16  17
  8                                      0   0   0   0   1   0   1   0   0
  9                                      0   0   0   0   0   0   0   0   0
  duct epithelial cell                   0   0   0   0   0   0   0   0   0
  endothelial cell of hepatic sinusoid   0   0   0   0   0   0   0   0   0
  hepatocyte                            96  96  86  78  76  72  56  43  41
                                      
                                        18  19  20
  8                                      0   0   0
  9                                     28   0   0
  duct epithelial cell                   0   0  26
  endothelial cell of hepatic sinusoid   0  27   0
  hepatocyte                             0   0   0

TSNEPlot(tiss, group.by="mouse.sex")

TSNEPlot(tiss, group.by="mouse.id")

Significant genes:

hepatocyte: Alb, Ttr, Apoa1, and Serpina1c pericentral: Cyp2e1, Glul, Oat, Gulo midlobular: Ass1, Hamp, Gstp1, Ubb periportal: Cyp2f2, Pck1, Hal, Cdh1

endothelial cells: Pecam1, Nrp1, Kdr+ and Oit3+ Kuppfer cells: Emr1, Clec4f, Cd68, Irf7 NK/NKT cells: Zap70, Il2rb, Nkg7, Cxcr6, Klr1c, Gzma B cells: Cd79a, Cd79b, Cd74 and Cd19 Immune cells: Ptprc

genes_hep = c('Alb', 'Ttr', 'Apoa1', 'Serpina1c',
                   'Cyp2e1', 'Glul', 'Oat', 'Gulo',
                   'Ass1', 'Hamp', 'Gstp1', 'Ubb',
                   'Cyp2f2', 'Pck1', 'Hal', 'Cdh1')
genes_endo = c('Pecam1', 'Nrp1', 'Kdr','Oit3')
genes_kuppfer = c('Emr1', 'Clec4f', 'Cd68', 'Irf7')
genes_nk = c('Zap70', 'Il2rb', 'Nkg7', 'Cxcr6', 'Gzma')
genes_b = c('Cd79a', 'Cd79b', 'Cd74')
genes_bec = c('Epcam', 'Krt19', 'Krt7')
genes_immune = 'Ptprc'

all_genes = c(genes_hep, genes_endo, genes_kuppfer, genes_nk, genes_b, genes_bec, genes_immune)

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest. The radius shows you the percent of cells in that cluster with at least one read sequenced from that gene. The color level indicates the average Z-score of gene expression for cells in that cluster, where the scaling is done over taken over all cells in the sample.

We have various immune cell types in the last cluster

Using the markers above, we can confidentaly label many of the clusters:

19: endothelial cells 20: bile duct epithelial cells 21: immune cells rest are hepatocytes

We will add those cell_ontology_classes to the dataset.

tiss <- StashIdent(object = tiss, save.name = "cluster.ids")

cluster.ids <- c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21)

free_annotation <- c(
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "endothelial cells",
  "bile duct epithelial cells",
  "immune cells")

cell_ontology_class <- c(
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
  "hepatocyte",
   "endothelial cell of hepatic sinusoid",
   "duct epithelial cell",
   "leukocyte")

tiss = stash_annotations(tiss, cluster.ids, free_annotation, cell_ontology_class)

The following `from` values were not present in `x`: 21
The following `from` values were not present in `x`: 21
The following `from` values were not present in `x`: 21

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = tiss, do.return = TRUE, group.by = "channel")

Subcluster

Let’s drill down on the hepatocytes.

subtiss = SubsetData(tiss, ident.use = c(0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18))

subtiss <- subtiss %>% ScaleData() %>%
  FindVariableGenes(do.plot = TRUE, x.high.cutoff = Inf, y.cutoff = 0.5) %>%
  RunPCA(do.print = FALSE)

[1] "Scaling data matrix"

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PCHeatmap(object = subtiss, pc.use = 1:3, cells.use = 20, do.balanced = TRUE, label.columns = FALSE, num.genes = 8)

PCElbowPlot(subtiss)

sub.n.pcs = 8
sub.res.use = 0.5
subtiss <- subtiss %>% FindClusters(reduction.type = "pca", dims.use = 1:sub.n.pcs,
    resolution = sub.res.use, print.output = 0, save.SNN = TRUE) %>%
    RunTSNE(dims.use = 1:sub.n.pcs, seed.use = 10, perplexity=8)

TSNEPlot(object = subtiss, do.label = T, pt.size = .5, label.size = 4)

BuildClusterTree(subtiss)

[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"
[1] "Finished averaging RNA for cluster 6"
[1] "Finished averaging RNA for cluster 7"
[1] "Finished averaging RNA for cluster 8"
[1] "Finished averaging RNA for cluster 9"

An object of class seurat in project Liver 
 23341 genes across 1792 samples.

From these genes, it appears that the clusters represent:

0: midlobular male 1: pericentral female 2: periportal female 3: periportal male 4: midlobular male 5: pericentral male 6: midlobular female 7: midlobular female

The multitude of clusters of each type correspond mostly to individual animals/sexes.

table(FetchData(subtiss, c('mouse.sex','ident')) %>% droplevels())

         ident
mouse.sex   0   1   2   3   4   5   6   7   8   9
        F   4 292 282   0   6   2 132 113   0  20
        M 315   0   0 204 171 150   0   0  93   8

sub.cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
sub.free_annotation <- c("midlobular male", "pericentral female", "periportal female", "periportal male", "midlobular male", "pericentral male", "midlobular female", "midlobular female")
sub.cell_ontology_class <- c("hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte", "hepatocyte")

subtiss = stash_annotations(subtiss, sub.cluster.ids, sub.free_annotation, sub.cell_ontology_class)
tiss = stash_subtiss_in_tiss(tiss, subtiss)

Checking for batch effects

Color by metadata, like plate barcode, to check for batch effects.

TSNEPlot(object = subtiss, do.return = TRUE, group.by = "mouse.sex")

Final coloring

Color by cell ontology class on the original TSNE.

TSNEPlot(object = tiss, do.return = TRUE, group.by = "cell_ontology_class")

Save the Robject for later

filename = here('00_data_ingest', '04_tissue_robj_generated', 
                     paste0("droplet_", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)

[1] "/home/rstudio/tabula-muris/00_data_ingest/04_tissue_robj_generated/droplet_Liver_seurat_tiss.Robj"

save(subtiss, file=filename)

# To reload a saved object
#filename = here('00_data_ingest', '04_tissue_robj_generated',
#                      paste0("droplet_", tissue_of_interest, "_seurat_tiss.Robj"))
#load(file=filename)

Export the final metadata

save_annotation_csv(tiss, tissue_of_interest, "droplet")